Anexo14.3 Formato de reacciones adversas
Anexo 14.4 Declaración de Helsinki
A Climate-Sensitive Matrix (CSMatrix) model controls for temperature (T) and precipitation (P) on tree growth, mortality and recruitment as follows (Ma et al. 2016):
R
y G
yt1 t(T,P) t t(T,P)ε (3-1) in which Gt is the growth matrix describing transition of trees between size classes as well as mortality. See Table B1 in the appendix for a complete list of variables.
The diameter growth of the kth tree of species i and size class j from t and t + 1 is represented by the following model:
ij Tree mortality, mijt, was a Probit function:
ij where Ф is the standard normal cumulative function.
Recruitment of species i, Ri is a Tobit model (Tobin, 1958):
) where Ф is the standard normal cumulative distribution function and φ is the standard normal
probability density function.
62 3.1.3 Simulation of Uncertain Fires
A mean fire interval (MFI) model developed by Guyette et al. (2010) and specifically designed for eastern and southern U.S. was used to simulate changes in fire frequencies induced by climate change, with the following equation:
d where C is a constant value (59.12) for average-intensity fire models. a is the mean maximum temperature (°C), b is the reciprocal moisture index (1/cm/°C), c is human population density (per km2), and d is the mean annual total precipitation (cm).
It is assumed that fire occurrence had an exponential distribution with its probability at time t being
p ( t ) 1 e
t/MFI. In the simulations, p(t) was uniformly distributed and drawn from 0 to 1 as a random variable. Thus, the t was calculated with –ln(1-p(t))MFI. Because fire has various impacts on species and size classes, five fire tolerance classes were designed to reflect differences in impacts on species and five fire susceptibility classes to reflect differences in effects on tree sizes within each species group (For details, see Ma et al. 2016).3.1.4 Estimation of Carbon Stocks
As part of my examinations of the ecological criteria, I quantified carbon storage in four pools in the CHR forests: aboveground biomass, fine roots, dead organic matters, and soil.
The single-stem volume by size and species (vij) was represented by the following model:
ij
See Table B2 in the appendix for a complete list of variables and estimated coefficients. The total volume was estimated as the product of the stem volume and the tree density. Tree stem
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biomass was them calculated as the total stand volume multiplied by wood density and 0.5 – the conversion factor for dry biomass (Birdsey 1992). The models of Jenkins et al. (2003) were applied to determine the biomass of other tree components.
Given no readily available biomass models for fine roots, they were assumed to be forty percent of foliage biomass (Helmisaari et al. 2007). Dead trees and annual litter production were used as input to the dead organic matters pool. Litter production was calculated from biomass using turnover rates in Liski et al. (2006). The initial chemical compositions of different dead matter inputs (Table B3) were obtained from Liski et al. (2009). Since physical size of litter affects decomposition rates (Tuomi et al. 2011a), litter from different tree compartments is added up on the basis of litter size class. For instance, litter from branches and coarse roots are under the fine woody litter size class. Litter size classifications of the seven species groups were shown in table B4. Finally, soil carbon was estimated with the Yasso07 model (Liski et al. 2009; Tuomi et al. 2011a; Tuomi et al. 2011b) which simulates the transitions between acid-soluble, water-soluble, ethanol-water-soluble, nonsoluble and humus components (AWENH-components and total summing up to 1) of the soil organic matter, as well as the decomposition of each component (Tuomi et al. 2011a).
3.1.5 Evaluation of Forest Management of Various Intensities
Economic Criteria
The economic criteria chosen here was the NPV of harvests over the planning period (2010 to 2100),
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where t was the harvesting cycle in years, xt = [xij]t, a column vector representing the percentage of trees per unit of land area of species group i (i=1,…,7) and diameter class j (j=1,…,17) at time t, n is number of trees, vij, a row vector in which vij was the volume of a single tree of species group i and diameter j. p represented the matrix of stumpage prices (Table 3-1), assumed constant over time, and r was the annual interest rate, assumed 3% here.
Ecological Criteria
Both species (Hs) and size (Hd) diversity were calculated with Shannon’s formulas (Pielou 1977):
where Bi, Bj and B were, respectively, the basal area of species group i, diameter class j and total basal area.
The total carbon stock (Q) was the total of carbon estimated in the aforementioned four pools:
Management regimes of low (20% of trees removed), medium (50%), and high (80%) harvesting intensities and explorative measures adaptive to species shift were described below. harvesting cycles of 10 and 20 years were, respectively, applied to each management regime.
1. Partial harvesting practices: harvesting trees with varying intensities across different diameter classes and species groups;
2. Diameter-limit harvesting: harvesting trees larger than 37cm in diameter with varying intensities across different diameter classes and species groups;
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3. Diameter-cap harvesting: harvesting trees smaller than 42cm in diameter with varying intensities across different diameter classes and species groups;
4. Adaptive measures: Adaptive1- harvest 50% of trees smaller than 42cm in diameter of QQ and QL, 20% of trees smaller than 42cm of JD, 5% of trees larger than 37cm of SD. Adaptive2 - harvest 10% of trees larger than 37cm in diameter of QQ and QL, 5% of trees larger than 37cm of JD, and 80% of trees smaller than 42cm of SD.
3.1.6 Fuzzy Sets Representing Uncertainty
Uncertain climate and wild fires led to high variability in predicted values of NPV, tree diversity, and carbon stocks. The averages of these predicted criteria are useful point estimations but to understand the associated risk, ranges or sets indicating uncertainty in predictions are essential.
Here I used fuzzy sets which involved defining membership functions that determined the level of uncertainty (Zadeh 1965). A trapezoidal fuzzy set was used, mathematically expressed as f (x;
a, b, c, d) = max (min (x – ab – a, 1, d – xd – c), 0). [b, c] represented the certainty interval for which the membership degree is 1. [a, b) and (c, d] were the uncertainty intervals with
membership degrees ranging from 0 to 1. [a, d] was a measure of total range of uncertainty arising from climate change and fire occurrences. Following Weckenmann and Schwan (2001), given the average value of one of the aforementioned criterion (𝑋̅ ) and its relative standard deviation (Sr) from simulations, a, b, c, d values can be constructed as follows:
b = 1+0.5S𝑋̅
𝑟
c = 𝑋̅(1 + 0.5Sr) a = b − 𝑋̅( 1+0.5S1
𝑟 – 1+2.5S1
𝑟 )
d = c + 𝑋̅∙2Sr (3-11)
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3.2 Results
3.2.1 Management of Various Intensities
When both changes of climate and fire regimes were simultaneously accounted for, on average, the climate scenario RCP2.6 resulted in the highest values for NPV, size diversity and total carbon stock under all management intensities, and the highest species diversity under most intensities. In year 2100, in general, the 20-year harvesting cycle led to higher total carbon stock and size diversity but lower NPV and species diversity. Low-intensity management caused the highest total carbon stock (10 years: 823 – 854 ton ha-1; 20 years: 864 – 888 ton ha-1) and size diversity (10 years: 1.93 – 2.11; 20 years: 1.95 – 2.10) but the lowest NPV (10 years: $ 9,318 –
$ 9,955 ha-1; 20 years: $ 3,426 – $ 4,056 ha-1) and species diversity (10 years: 1.28 – 1.31; 20 years: 1.18 – 1.22). Lower total carbon stock (10 years: 778 – 814 ton ha-1; 20 years: 800 – 828 ton ha-1) were expected with medium intensity but satisfactory species diversity (10 years: 1.50 – 1.53; 20 years: 1.36 – 1.39), size diversity (10 years: 1.47 – 1.59; 20 years: 1.91 – 2.02), and NPV (10 years: $ 18,721 – $ 19,812 ha-1; 20 years: $ 7,749 – $ 9,596 ha-1). High intensity
resulted in the lowest total carbon stock (10 years: 740 – 775 ton ha-1; 20 years: 768 – 794 ton ha
-1) and size diversity (10 years: 0.89 – 1.02; 20 years: 1.27 – 1.40), but the highest NPV (10 years:
$ 26,749 – $ 27,440 ha-1; 20 years: $ 13,302 – $ 13,757 ha-1) and species diversity (10 years:
1.58 – 1.61; 20 years: 1.53 – 1.56) (Tables 3-2, 3-3, 3-4).
Diameter-limit and diameter-cap harvesting with low, medium, and high intensities displayed similar trends as partial harvesting practices for NPV of harvests, size diversity, and carbon stocks, while diameter-cap harvesting with high intensity had lower species diversity. The NPV of harvests and species diversity with a 10-year harvesting cycle was more than with 20
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years, but carbon stocks were only 1.3% – 5.0% lower. A 10-year harvesting cycle produced lower size diversity than with a 20-year cycle under most intensities. In addition, soil carbon made up approximately 80% of total carbon stock and displayed relatively low variability in response to harvesting intensities (Table 3-4).
Table 3-2Average net present value ($ ha-1) for low, medium, and high intensities of partial harvesting, diameter-limit and diameter-cap harvesting, and two adaptive measures from 2010 to 2100.
Management regimes RCP2.6 RCP4.5 RCP6.0 RCP8.5 Harvesting cycle (10 years)
Partial harvesting (low) 9,955 9,594 9,425 9,318 Partial harvesting (medium) 19,812 19,361 19,018 18,721 Partial harvesting (high) 27,440* 27,233 27,034 26,749 Diameter-limit (low) 4,655 4,442 4,298 4,059 Diameter-limit (medium) 9,738 9,461 9,290 9,058 Diameter-limit (high) 12,938 12,747 12,535 12,063 Diameter-cap (low) 7,046 6,872 6,727 6,402 Diameter-cap (medium) 15,112 15,048 14,823 14,531 Diameter-cap (high) 21,547 21,395 21,193 20,857
Adaptive1 14,762 14,212 13,964 13,714
Adaptive2
Harvesting cycle (20 years) Partial harvesting (low)
* Numbers in bold were the highest values among all management regimes.
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Table 3-3Average tree diversity for low, medium, and high intensities of partial harvesting, diameter-limit and diameter-cap harvesting, and two adaptive measures in 2100.
Management regimes RCP2.6 RCP4.5 RCP6.0 RCP8.5 Harvesting cycle (10 years)
Species diversity
Harvesting cycle (20 years) Species diversity
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Table 3-4Average total carbon and soil carbon (ton ha-1) for low, medium, and high
intensities of partial harvesting, diameter-limit and diameter-cap harvesting, and two adaptive measures in 2100.
Total carbon Soil carbon
Management regime RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 Harvesting cycle (10 years)
Partial harvesting (low) 854 845 834 823 681 675 668 659
Harvesting cycle (20 years) Partial harvesting (low)
* Numbers in bold were the highest values among all management regimes.
3.2.2 Measures Adaptive to Species Shift
As expected, the projected outcomes of two adaptive measures demonstrated completely
different patterns over the next 90 years. NPV, species diversity, size diversity and carbon stocks of Adaptive1, which entailed intensive harvesting of oak species and maintaining maple species, were $ 14,762 ha-1, 1.06, 2.09, and 941 ton ha-1 for the climate scenario RCP2.6 with harvesting cycle in 10 years, respectively, while the climate scenario RCP8.5 had $ 13,714 ha-1, 1.04, 1.93, and 905 ton ha-1 under the same harvesting cycle (Tables 3-2, 3-3, 3-4). In addition, the RCP4.5 had $ 6,394 ha-1, 1.06, 2.06, 951 ton ha-1 and the RCP6.0 had $ 6,128 ha-1, 1.07, 2.04, 945 ton ha
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1 with harvesting cycle in 20 years over the next 90 years, respectively (Tables 3-2, 3-3, 3-4).
With Adaptive2 of intensive harvesting of maples, NPV, species diversity, size diversity, and carbon stocks under four climate scenarios converged to $ 2,423 – $ 2,668 ha-1, 1.28 – 1.33, 1.63 – 1.80, and 860 – 890 ton ha-1 when harvested every 10 years, and $ 815 – $ 1,017 ha-1, 1.28 – 1.31, 1.67 – 1.86, and 887 – 910 ton ha-1 when the harvesting cycle doubled, in 2100,
respectively (Tables 3-2, 3-3, 3-4). To summarize, Adaptive1 led to higher NPV (453% – 625%), size diversity (14.5% – 18.4%) and carbon stocks (4.1% – 5.8%), but lower species diversity (14.7% – 20.3%), than Adaptive2 with both harvesting cycles. It was also worth noting that Adaptive1 performed better than diameter-limit, diameter-cap and partial harvesting practices in terms of total carbon stocks. The two adaptive regimes exhibited totally different species
composition at the end of 21st century. Under Adaptive1, maple trees accounted for 38.3% – 45.5% of the total aboveground biomass, while oak trees made up 34.6% – 43.1% in all four climate scenarios (Figure 3-1). When adopting Adaptive2, maple trees only made up 1.1% – 3.1%, but oak trees maintained their dominance in the total aboveground biomass, ranging from 62.8% – 72.9% under four climate scenarios.
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Figure 3-1 Percentages of above-ground biomass in seven species groups under management regimes Adaptive1 and Adaptive2 and with harvesting cycles of 10 and 20 years, respectively.
QQ: Quercus–Quercus (white oak species), QL: Quercus–Lobatae (red oak species), JD:
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Juglandaceae (hickory), SD: Sapindaceae (maple family), GS: Gymnosperms (softwoods), FG:
Fagus (American beech), OA: Other Angiosperms (other species).
3.2.3 Uncertainty Analysis
To account for variability in the simulation results, fuzzy sets were constructed for all management criteria based on equation 11 (Figures 3-2, 3-3, 3-4, 3-5) for the two harvesting cycles, respectively. Using a 10-year harvesting cycle, partial harvesting with low intensity and Adaptive1 clearly outperformed the other regimes financially under RCP 2.6, with high certainty.
Adaptive 2 led to the lowest NPV with high certainty under RCP 8.5. Medium and high
intensities could lead to similar NPVs under four climate scenarios, given the amount of overlap among the fuzzy sets. With a 20-year cycle, it was highly certain that the lowest NPV would occur under RCP 8.5 for all regimes except for Adaptive1 and medium-intensity harvesting would generate the highest NPV under RCP 2.6. Also with high certainty, RCP 2.6 would lead to higher NPV under all regimes than RCP 6.0 and 8.5. However, it is possible that all regimes except for medium-intensity harvesting would produce similar NPVs under RCP 2.6 and 4.5 (Figure 3-2).
In addition, when harvesting trees every 10 years, Adaptive1 led to the least species diversity with high certainty under RCP6.0 and Adaptive2 caused the lowest under RCP8.5 (Figure 3-3). All management regimes would lead to the lowest size diversity with high certainty under RCP8.5 (Figure 3-4). According to the overlaps among the fuzzy sets, low, medium, and high intensities could result in similar total carbon stocks under four climate scenarios.
Adaptive2 would have the least total carbon stock with high certainty under RCP8.5 (Figure 3-5). When the harvesting cycle doubled, as shown in Figure 3, medium- and high-intensity harvesting practices could lead to similar species diversity under different climate scenarios,
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while RCP 4.5 resulted in much lower species diversity than the other scenarios under low-intensity regime. RCP 8.5 would generate the highest species diversity with high confidence under Adaptive1. Partial harvesting with low intensity, Adaptive1 and Adaptive2 could lead to the lowest size diversity with high certainty under RCP8.5 (Figure 3-4). However, all
management regimes could lead to similar total carbon stocks under four climate scenarios based on the obvious overlap among the fuzzy sets (Figure 3-5).
In sum, there were no overlaps between fuzzy sets of NPV, size diversity and total carbon stock under RCP2.6 and RCP8.5 with both harvesting cycles (Figures 3-2, 3-4, 3-5), indicating that when considering uncertain climate and fire, these criteria would be distinctively different in RCP2.6 and RCP8.5 with high certainty. However, for species diversity, the existing overlaps among four climate scenarios (Figure 3-3) suggested the possibility of similar climatic effects on species diversity across most management regimes, when taking account of uncertainty.
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Figure 3-2 Fuzzy sets representing uncertainty in the NPV of low, medium, and high
intensities of partial harvesting, adaptive1 and adaptive2 with harvesting cycles of 10 and 20 years from 2010 to 2100.
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Figure 3-3 Fuzzy sets representing uncertainty in the species diversity of low, medium, and high intensities of partial harvesting, adaptive1 and adaptive2 with harvesting cycles of 10 and 20 years in 2100.
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Figure 3-4Fuzzy sets representing uncertainty in the size diversity of low, medium, and high intensities of partial harvesting, adaptive1 and adaptive2 with harvesting cycles of 10 and 20 years in 2100.
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Figure 3-5 Fuzzy sets representing uncertainty in the carbon stocks of low, medium, and high intensities of partial harvesting, adaptive1 and adaptive2 with harvesting cycles of 10 and 20 years in 2100.
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3.3 Discussion and Conclusion
In this study, I applied a climate-sensitive matrix model to quantify economic and ecological impacts of various harvesting intensities (20% - 80%) in the CHR when fire intervals were predicted to be considerably shortened by a changing climate. It is vital to consider climate-induced alterations to fire regimes to identify the way in which forest management affects stand carbon stocks (Rubio et al. 2011) and other ecosystem services. Nevertheless, existing dynamic global vegetation models such as, MC1 (Bachelet et al. 2001), LPJ (Sitch et al. 2003) and ORCHIDEE (Krinner et al. 2005), simulate ecosystem processes at the continental extent and thus do not capture frequent low-intensity fires and species-specific processes, such as tree mortality and regeneration. They, however, are important bottom-up forces on carbon balance (Loehman et al. 2014) and are essential for estimating stand-level tree growth and yield.
Management intensity is important for determining optimal carbon sequestration in managed forest ecosystems (Cooper 1983; Parker et al. 2000; Taylor et al. 2008), but it directly influences the financial return of harvested timber. 20% of trees removal management may be more effective than 50% and 80% to enhance carbon sequestration at the expense of lower income for landowners. Similarly, harvesting treatments that maintain a large proportion of larger-diameter trees could be superior, in terms of maintaining carbon stocks, to those
associated with more intensive removals (Harmon et al. 2009; Keyser 2010; Taylor et al. 2008), but lead to lower NPV of harvests. My simulation results agreed with these findings. The diameter-cap harvesting stored the most carbon, followed by diameter-limit and partial
harvesting practice, also consistent with previous results (Harmon and Marks 2002; Peng et al.
2002).
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My work estimated forest carbon stocks in four pools. The soil organic carbon was predicted with YASSO07, which is applicable to both temperate and boreal forests (Liski et al.
2006). It was shown that soil organic carbon was highly resilient to varying intensities and accounted for nearly 80 percent of total stand carbon, thus in line with the previous studies arguing that harvesting does not significantly affect soil carbon (Johnson and Curtis 2001;
Rashid 2013; Yanai et al. 2003). The result also indicated that appropriate management treatments may maintain or enhance forest carbon stocks, as opposed to no management,
consistent with McKinley et al. (2011) and Stephens et al. (2012). My study further revealed that the NPV of harvests with a 10-year harvesting cycle was more than twice of 20 years, but carbon stocks were only 1.3 % – 5.0 % lower for all management regimes. This suggested that more frequent harvests might produce higher NPVs without causing significant reductions of carbon stocks. The carbon stored in harvested wood products was not under consideration in this study and calls for an examination in the future.
Maintaining and increasing species and size diversity in forest stands have become a recent focus of forest management related to climate change adaptation (D’Amato et al. 2011;
Puettmann et al. 2009; Liang et al. 2015). Ecosystems with low levels of diversity may be more vulnerable to potential changes in climate and disturbance regimes (Seidl et al. 2011). This study showed that 80% of trees removal may result in 18.6% – 22.9% greater species diversity but 18.1%
– 25.7% lower size diversity than 50% and 20%. One possible explanation is that light gaps from intensive removals increased growth of shade-tolerant species and reduction in large-diameter trees decreased structural diversity. Warmer climates may have similar consequences on species and size diversity, probably because they caused more frequent fires that mimicked the effects of intensive harvests.
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I recognize that there is uncertainty in model projections related to climate change and fire disturbance (Nunery and Keeton 2010). Fire disturbance could impact carbon sequestration through rapid flux of carbon from living biomass to dead organic matters. In response to warmer and drier conditions, fire regimes are projected to alter in the coming decades not only in terms of shortened fire intervals, but also of prolonged fire season length and increased cumulative area burned (Flannigan et al. 2006; McKenzie et al. 2004). In my study, hindered by the complexity of modeling fundamental fire processes in forests including fuel particle ignition and fire spread, I only examined the climatic impacts on fire frequencies. Moreover, fires confer many important ecological benefits not discussed in this study. Besides, I did not account for wind damage, insect, disease, and other natural disturbances. Hence the model presented here had limited predictive power thus caution should be used to interpret the simulation results. On the other hand, more uncertainty is expected to rise in the projections when taking account of these missing aspects partly due to the incomplete knowledge of climate change and associated disturbances. How to take these sources of uncertainty into decision and policy making largely remains an open issue.
Tradeoffs between multiple management objectives are often necessary as enhancing one objective (economic or ecological) may inevitably compromise the others. For example, as shown here, 20% of trees removal led to higher carbon stocks but lower NPV while 80% of trees removal behaved the opposite way. Balancing economic and ecological objectives requires a constrained optimization paradigm. More detailed analyses assisted with stochastic optimization could examine what harvesting intensity optimizes ecological objectives while providing a satisfactory level of NPV. Determining the opportunity cost of carbon sequestration in forests will be one key to addressing the societal needs for environmental sustainability and economic viability simultaneously. That is what my future research will focus on.
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Acknowledgement
This study was supported by the Davis College of Agriculture, Natural Resources & Design, West Virginia University, under the US Department of Agriculture (USDA) McIntire–Stennis Funds WVA00105.
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